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• W2166857098 abstract "A multistage identification scheme for structural damage detection using time domain acceleration responses is proposed. Previous studies of damage assessment using neural networks mostly involved training a backpropagation neural network (BPN) to learn damage patterns with significant computational effort. A hybrid neural network method has been proposed that uses a counterpropagation neural network (CPN) in the first stage for sorting the training data into clusters, giving an approximate guess of the damage extent quickly. After an approximate estimate is obtained, a new set of training patterns of reduced size is generated using the CPN prediction. In the second stage, a BPN trained with the Levenberg–Marquardt algorithm is used to learn the new training data and predict a more accurate result. A superior convergence and a substantial decrease in central processing unit time have been observed for three numerical examples. These examples show the computational superiority of the hybrid method compared..." @default.
• W2166857098 created "2016-06-24" @default.
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• W2166857098 date "2009-02-01" @default.
• W2166857098 modified "2023-09-24" @default.
• W2166857098 title "A hybrid neural network strategy for the identification of structural damage using time domain responses" @default.
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• W2166857098 doi "https://doi.org/10.1080/19373260802193374" @default.
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